The matplotlib library allows you to visualize data using various graphing techniques. It has built-in default and flexibility to match your needs. After importing the required modules and preparing your data, you can plot your data using the plot() and show() functions. The matplotlib library also comes with a pyplot module, imported under the alias.
Customization options
Matplotlib comes with default settings that you can change to suit your preferences. You can also customize the library and plots with the help of style sheets. These settings can be changed in the RC file, which is read during Matplotlib startup. In this tutorial, you will learn about some of the customization options that you can use to get more from Matplotlib.
Customization options allow you to change the colours and markers on your plots. You can also add a legend or change the font colour. You can even adjust the transparency. There are many more customization options that you can use to get your plots looking the way you want them. In this tutorial, you’ll discover how to customize your stories.
Customization options are built-in to Matplotlib, which means you can use them for publication and interactive visualization. In general, you’ll use one of these style conventions.
Plotting data as-is
When plotting data with Matplotlib, you’ll want to use the RC file, which allows you to make adjustments both statically and dynamically. You can read more about adjusting the colour map in the matplotlib documentation. If you want to plot data differently, use the RC file to reverse the colours.
In this tutorial, we will use the plot method to plot data from the pandas Series object. Initially, this method plots the values as-is. This makes the data look cramped, but we can fix this by rotating the axes. This method also allows us to draw grids within plots.
Another essential feature of the Matplotlib library is its ability to support multiple plots on a single “window” or “figure.” We need to define a “subplot” object to use the subplot feature. Then, we can call the add_subplots() method.
Saving plots to an image file
Saving plots to an image file is a common task and can be done with the help of the matplotlib function save fig(). This function will save the plot as a png image file. This image file can be opened in a graphics program or dedicated to the system’s memory. When using this method, you should specify the name and location of the file, as well as the file format and extension.
The save fig() function accepts the output file as its first argument. You can provide a relative or full path to the image file. The output image will be saved in the current working directory if no way is provided. This feature ensures backwards compatibility with other programs. You can also specify the file format and name extension by setting the format parameter. By default, the format parameter specifies the image file type to be saved.
When saving matplotlib figures to an image file, you can choose a format that best suits the output of your data. For example, JPG files are a better option because they have smaller file sizes and are more compressed. Set the export options to quality, optimize, or progressive to reduce file size and increase the rate. These settings reduce the file size by 50%. However, it’s better to choose a vector-based SVG or PDF format for the best quality and the smallest file size.
Creating stacked line graphs
A stacked line graph is a type of chart that displays multiple line values. To create one, you need three columns of data: one independent variable, two series of dependent variables, and time. This type of chart is cumulative, meaning that the top line represents the sum of all the percentages of the data lines below it. As a result, the lines do not cross each other as in a regular line graph.
To create stacked line graphs, you’ll need to know how to use the Matplotlib library. This package is compelling, taking care of the basic plotting needs of Python programs. Its interface is similar to that of the MATLAB programming language. It also integrates well with the panda’s package, which can help you wrangle data and create valuable insights.
Stacking line graphs can be handy for displaying the contribution of each column in a data set. Unlike bar charts, they do not need a zero-baseline to show how each line contributes to the overall trend. Some visualization tools require you to include columns that list the contributions of each group. Using this method, the height of the individual coloured regions is calculated from the difference between the two columns.
